Detecting evolutionary stages of karst depressions in Central Brazil with deep learning and Planet NICFI time series

Abstract Karst depressions represent significant geomorphological features crucial in environmental monitoring and conservation. This research aims to detect and classify karst depressions according to their evolutionary stages in the Bambuí Group, located between the Cerrado and Caatinga biomes in Central Brazil, using Planet time series provided by Norway's International Climate and Forest Initiative (NICFI) Satellite Data Program and deep learning‐based semantic segmentation models. A new deep learning training dataset was developed containing manually labelled reference data and a time series of monthly images from Planet NICFI data over a year. The research classified three evolutionary stages of karst depressions: (1) temporary lakes, (2) depressions with concentric halos and subsidence and (3) vegetated depressions. These stages represent distinct geomorphological processes, from initial water accumulation to more advanced stages involving subsidence and vegetation development in the depression areas. The study compared six state‐of‐the‐art semantic segmentation architectures (U‐Net, U‐Net++, DeepLabV3+, LinkNet, FPN and PSPNet), each combined with three backbones (EfficientNet‐B7, ResNet‐101 and ResNeXt‐101), resulting in 18 model configurations. The best performing model (U‐Net with EfficientNet‐B7) achieved a mean Intersection over Union (mIoU) of 80 and IoU scores of 97.9 for the background, 80.93 for first stage, 79.89 for second stage and 63.35 for third stage, highlighting the challenges of detecting more advanced stages due to increasing vegetation cover and geomorphological complexity. The sliding window approach was employed to classify the entire image mosaic, testing various stride values (8, 16, 32, 64, 128 and 256), with smaller strides improving segmentation accuracy at the cost of higher computational demands. The results demonstrate the importance of integrating spectro‐spatio‐temporal data to detect multiple evolutionary stages and improve the robustness of semantic segmentation. This research provides a comprehensive dataset and benchmark for future studies on karst depressions, contributing to understanding geomorphological evolution and conservation planning in Central Brazil.